Project Summary In this goal-driven proposal, submitted in response to Funding Opportunity Announcement (FOA) Number PAR-11-003, we will make comprehensive genetic testing for deafness available to clinicians for under $500 per person. The driving force behind this initiative is the frequency of hearing impairment. As the most common sensory impairment, it is diagnosed in 1 of every 500 newborns and 50% of octogenarians (Morton Ann N Y Acad Sci 1991). With 57 genes implicated in nonsyndromic hearing loss (NSHL), it is also an extremely heterogeneous trait and presents a tremendous challenge to diagnosis. Current strategies for genetic testing for deafness are inadequate. For most, only a minority of genes is included, with selection criteria typically reflecting: 1) high prevalence as a cause of deafness (i.e. GJB2); 2) association with another recognizable feature (i.e. SLC26A4 and enlarged vestibular aqueduct); or 3) a recognizable audioprofile (i.e. low frequency hearing loss as seen with WFS1) (Hilgert et al Mut Res 2009). The recent advent of powerful DNA target enrichment and sequencing technologies, however, makes it possible to provide comprehensive genetic testing for deafness that is efficient and cost-effective. We have shown that it is possible to analyze all deafness genes simultaneously on a single platform (called OtoSCOPE) (Shearer et al PNAS 2010). Related to this endeavor, we have also validated AudioGene as a phenotypic tool that uses patient audiograms to predict the genetic cause of ADNSHL (Hildebrand et al Genet Med 2008; Hildebrand et al Laryngoscope 2009). Building on these findings, in this proposal we will complete two specific aims. Specific Aim 1: To provide comprehensive, high-throughput, low-cost DNA sequence generation and analysis for deafness genetic testing Goal 1: Comprehensive, high-throughput, low-cost DNA sequence analysis for genetic testing for deafness is possible at sensitivities and specificities comparable to Sanger sequencing by using targeted sequence enrichment followed by massively parallel sequencing. Specific Aim 2: To optimize both machine learning-based audioprofiling of audiometric data and phenotypic filtering of genotypic data by expanding and improving the platform we have developed called AudioGene Goal 2: As a phenome tool, a machine-learning software system trained on an extensive set of audiometric data can be used to predict and to eliminate specific genes or gene variants as causes of deafness based on audiometric data. Achieving these specific aims will change the clinical evaluation of deaf and hard-of-hearing persons by making genetic testing the most important diagnostic test after a history, physical examination and audiological assessment.